Following the literature on domain adaptation of semantic segmentation models, we use Deeplab-V2 [1]} with ResNet-101 [2]} as the backbone. We use one GPU to train source models with a batch size of 1 in all experiments. We use SGD with an initial learning rate of \(2.5\times 10^{-4}\)  with polynomial decay of power \(0.9\)  [1]}. We use the standard metric of mean intersection over union (mIoU) [1]} to evaluate. Note that we use only one augmented image and prior (\(\lambda =0.8\) ) in all experiments on segmentation, unless otherwise mentioned. We use Gaussian blur and random rotation as augmentations, i.e., with \(k=m=2\)  (Algorithm REF ).
